Performance Analysis of Kernel Function for Support Vector Machine

Support Vector Machine에 대한 커널 함수의 성능 분석

SVM(Support Vector Machine) is a classification method which is recently watched in mechanical learning system. Vapnik, Osuna, Platt etc. had suggested methodology in order to solve needed QP(Quadratic Programming) to realize SVM so that have extended application field. SVM find hyperplane which classify into 2 class by converting from input space converter vector to characteristic space vector using Kernel Function. This is very systematic and theoretical more than neural network which is experiential study method. Although SVM has superior generalization characteristic, it depends on Kernel Function. There are three category in the Kernel Function as Polynomial Kernel, RBF(Radial Basis Function) Kernel, Sigmoid Kernel. This paper has analyzed performance of SVM against kernel using virtual data.